Background Subtraction for Automated Multisensor Surveillance: A Comprehensive Review
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Review Article Background Subtraction for Automated Multisensor Surveillance: A Comprehensive Review Marco Cristani,1, 2 Michela Farenzena,1 Domenico Bloisi,1 and Vittorio Murino1, 2 1 Dipartimento 2 IIT
di Informatica, University of Verona, Strada le Grazie 15, 37134 Verona, Italy Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
Correspondence should be addressed to Marco Cristani, [email protected] Received 10 December 2009; Accepted 6 July 2010 Academic Editor: Yingzi Du Copyright © 2010 Marco Cristani et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background subtraction is a widely used operation in the video surveillance, aimed at separating the expected scene (the background) from the unexpected entities (the foreground). There are several problems related to this task, mainly due to the blurred boundaries between background and foreground definitions. Therefore, background subtraction is an open issue worth to be addressed under different points of view. In this paper, we propose a comprehensive review of the background subtraction methods, that considers also channels other than the sole visible optical one (such as the audio and the infrared channels). In addition to the definition of novel kinds of background, the perspectives that these approaches open up are very appealing: in particular, the multisensor direction seems to be well-suited to solve or simplify several hoary background subtraction problems. All the reviewed methods are organized in a novel taxonomy that encapsulates all the brand-new approaches in a seamless way.
1. Introduction Video background subtraction represents one of the basic, low-level operations in the video surveillance typical workflow (see Figure 1). Its aim is to operate on the raw video sequences, separating the expected part of the scene (the background, BG), frequently corresponding to the static bit, from the unexpected part (the foreground, FG), often coinciding with the moving objects. Several techniques may subsequently be carried out after the video BG subtraction stage. For instance, tracking may focus only on the FG areas of the scene [1–3]; analogously, target detection and classification may be fastened by constraining the search window only over the FG locations [4]. Further, recognition methods working on shapes (FG silhouettes) are also present in the literature [5, 6]. Finally, the recent coined term of video analytics addresses those techniques performing high-level reasoning, such as the detection of abnormal behaviors in a scenery, or the persistent presence of foreground, exploiting low-level operations like the BG subtraction [7, 8]. Video background subtraction is typically an online operation generally composed by two stages, that is, the
background initialization, where the model of the background is bootstrapped, and background maintenance (or updatin
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